Abstract 4394: Feasibility analysis of querying EHR databases for cancer clinical trial criteria
Authors: Dylan J. Cooper, Joseph Herman, Nalan Yurtsever, Sergio Garza, Daniel A. King, Yonah Ziemba
Journal: Cancer Research (2023)

Abstract Background: In order to enroll patients for clinical trials (CT), it is common to run queries in clinical databases using CT eligibility criteria as filters and specifications. However, CT eligibility criteria are often unavailable as discrete data fields, which can interfere with CT recruitment. The purpose of this study was to identify the most commonly needed eligibility criteria that are specified in cancer CT to investigate whether and how they could be found in clinical databases. Design: We examined the data elements needed for the trial eligibility criteria that would be used in queries for all the Breast, Gastrointestinal, Genitourinary, Head and Neck, and Cutaneous Cancer treatment trials open for enrollment across our health system. We then performed a sort-rank exercise for these eligibility criteria terms. Results: The analysis included 33 trials and 12 data elements. Table 1 shows the number of times each eligibility criterion appeared in the 33 CTs and its feasibility for an EHR query. The data elements that appeared most frequently were Organ/Cancer Site (100%), Tumor Morphology (66.7%), Presence of Metastases (51.5%), TN Staging (39.4%), and Days Since Diagnosis (24.2%). Of these, only Organ/Cancer Site and Days Since Diagnosis were accessible as structured datapoints in our clinical databases. Conclusions: The lack of feasibility for EHR query is likely due to the fact that healthcare information is generally stored in a discrete, structured form as a means of completing care transactions (drug orders or laboratory values) and ensuring billing compliance (ICD and outpatient CPT codes). This presents a critical challenge when data are required for CT enrollment, quality assurance metrics, and prospective and retrospective clinical research. Health systems—and by extension, their patients—would benefit from investing in structured data schemas involving AJCC staging, tumor morphology, and presence of metastases. Eligibility criteria with corresponding number of appearances in CTs and feasibility for EHR query Eligibility criterion Number of trials with each criterion Feasibility for EHR query Organ/Cancer Site 33 (100%) Accessible (ICD codes very detailed for site and used consistently for purpose of billing) Tumor Morphology 22 (66.7%) Not accessible (Code have not been created in ICD system, SNOMED is not widely adopted, not needed for billing) Presence of Metastases 17 (51.5%) Not accessible (Not coded consistently, coded in ICD as a “secondary” malignancy but generally does not affect billing) TN Staging 13 (39.4%) Not accessible (Not coded in ICD system) Days Since Diagnosis 8 (24.2%) Accessible (Structured data captured for purpose of initial transaction) Tumor Markers and Other Lab Results 4 (12.1%) Accessible (Structured data captured by laboratory instrument for purpose of initial transaction) Immunohistochemical Biomarkers 4 (12.1%) Not accessible (Pathologist assessment documented as narrative text) History of Systemic Therapy 3 (9.1%) Accessible (NDC drug code captured with initial order in order to complete the initial transaction) Feasibility of Resection 3 (9.1%) Not accessible (Surgeon assessment documented as narrative text) Disease progression 3 (9.1%) Not accessible (Radiologist assessment documented as narrative text) Surgical Procedure 2 (6.1%) Accessible as CPT codes for outpatient surgeries as needed for billing. Not accessible for inpatient surgeries where billing is capitated as a single fee for entire hospitalization based on disease related groups Margins 2 (6.1%) Not accessible (Pathologist assessment documented as narrative text) Citation Format: Dylan J. Cooper, Joseph Herman, Nalan Yurtsever, Sergio Garza, Daniel A. King, Yonah Ziemba. Feasibility analysis of querying EHR databases for cancer clinical trial criteria. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4394.